Pushing the envelope of seismic stratigraphic interpretation: a case-study from the Mannville Group using small 3-D surveys
Bibliographic record
Abstract
Abstract In this paper, we integrate 3-D seismic and wireline log data to illustrate some modern techniques of seismic stratigraphy. The imaging target is the Lower Cretaceous Mannville Group, traditionally one of the main targets for hydrocarbon exploration and production in Western Canada. The depositional environments for the Mannville Group in our study area were diverse, ranging from fluvial to shallow marine. Mannville Group strata overlie a major unconformity that separates them from predominantly carbonate rocks of the Paleozoic (Devonian). As a first step, we integrated wireline logs and seismic amplitude data in a qualitative way and gained more stratigraphic insights than could be obtained with either data set alone. We then used seismic inversion and a seismic attribute study to make quantitative lithology predictions. Acoustic impedance inversion proved to be an excellent tool for mapping the unconformity, clearly distinguishing the clastic rocks above from the carbonate-dominated units below it. However, the inversion result did not clearly define stratigraphic features within the Mannville Group. To that end, we generated a pseudo-lithology (gamma-ray) volume for the Mannville Group by integrating wireline logs and seismic attributes using a neural network. This pseudo-lithology volume identified stratigraphic features that were not apparent in either the original seismic amplitude or the inversion volume. The results of this study show how integrating the three different 3-D seismic versions was useful for understanding the stratigraphic complexity of the Mannville Group. The approach presented here can be used for similar purposes in other geological settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".